Conclusion

Differential Expression Analysis with limma in R

John Blischak

Instructor

Pre-processing features

Visualize with plotDensities
3 steps:
- Log transform
- Normalize
- Filter

Visualize single genes with boxplot

top2b <- which(fData(eset)[, "symbol"] == "Top2b")

boxplot(
  exprs(eset)[top2b, ] ~ pData(eset)[, "genotype"],
        main = fData(eset)[top2b, ]
)

Check sources of variation

# Plot principal components labeled by genotype
plotMDS(eset, labels = pData(eset)[,"genotype"], gene.selection = "common")
# Plot principal components labeled by treatment
plotMDS(eset, labels = pData(eset)[,"treatment"], gene.selection = "common")

Flexibly testing various study designs

# Create single variable
group <- with(pData(eset), 
              paste(genotype, treatment, sep = "."))
group <- factor(group)

# Create design matrix with no intercept
design <- model.matrix(~0 + group)
colnames(design) <- levels(group)

# Create a contrasts matrix
cm <- makeContrasts(dox_wt = wt.dox - wt.pbs,
           dox_top2b = top2b.dox - top2b.pbs,
        interaction = (top2b.dox - top2b.pbs) -
                                 (wt.dox - wt.pbs),
                    levels = design)

# Fit the model
fit <- lmFit(eset, design)

# Fit the contrasts
fit2 <- contrasts.fit(fit, contrasts = cm)

# Calculate the t-statistics for the contrasts
fit2 <- eBayes(fit2)

Histograms of p-values

topTable and hist

Volcano plots

# Extract the gene symbols
gene_symbols <- fit2$genes[, "symbol"]
# Create a volcano plot for the contrast dox_wt
volcanoplot(fit2, coef = "dox_wt", highlight = 5, names = gene_symbols)

# Extract the entrez gene IDs
entrez <- fit2$genes[, "entrez"]
# Test for enriched KEGG Pathways for contrast dox_wt
enrich_dox_wt <- kegga(fit2, coef = "dox_wt", geneid = entrez,
                       species = "Mm")
# View the top 4 enriched KEGG pathways
topKEGG(enrich_dox_wt, number = 4)

Pathway
path:mmu05322                           Systemic lupus erythematosus
path:mmu03008                      Ribosome biogenesis in eukaryotes
path:mmu05034                                             Alcoholism
path:mmu05412 Arrhythmogenic right ventricular cardiomyopathy (ARVC)
                N Up Down         P.Up       P.Down
path:mmu05322  76 37    1 3.657708e-10 9.999999e-01
path:mmu03008  71 34    4 3.320811e-09 9.997410e-01
path:mmu05034 130 47   17 2.358456e-07 9.733029e-01
path:mmu05412  52  2   26 9.995025e-01 4.140466e-07

Congrats on completing the course!

Differential Expression Analysis with limma in R